Motion-adaptive Duty-cycling to Estimate Orientation using Inertial Sensors

We present a motion-adaptive duty-cycling approach to estimate orientation using inertial sensors. In particular, we deploy a proportional forward-controller to adjust the duty-cycle of inertial sensing units (IMU) and the orientation estimation update rate of an extended Kalman filter (EKF). In sample data recordings and a simulated daily life dataset from a wrist-worn IMU, we show that our motion-adaptive approach incurs substantially lower errors that a static duty-cycling approach. During phases with low or no rotation motion, as it is often occurring in daily activities, our approach can dynamically reduce the IMU operation to 20% of the regular rate. Results show that duty-cycles of 50% are common during low-wrist rotation activities, such as reading and typing, while orientation error is below 1$\degree$. We further show the power saving benefits of our approach in a case study of the ETHOS IMU device.